U.S. patent number 5,065,229 [Application Number 07/588,775] was granted by the patent office on 1991-11-12 for compression method and apparatus for single-sensor color imaging systems.
This patent grant is currently assigned to Eastman Kodak Company. Invention is credited to Kenneth A. Parulski, Majid Rabbani, Yusheng T. Tsai.
United States Patent |
5,065,229 |
Tsai , et al. |
November 12, 1991 |
Compression method and apparatus for single-sensor color imaging
systems
Abstract
The method of the present invention operates upon the imaging
signals from a color filter array image sensor to compress the
image signals prior to color interpolation by separating the
signals into three color planes. Missing green pixels are recovered
through interpolation methods, either by bilinear or other
techniques, in order to form color ratio signals long
B/G.sub.missing and log R/G.sub.missing which allows for proper
interpolation after decompression without color artifacts. Upon
playback or reception, an inverse compression is applied and
missing green, red and blue pixels are interpolated to provide
three full planes of color image data.
Inventors: |
Tsai; Yusheng T. (Rochester,
NY), Parulski; Kenneth A. (Rochester, NY), Rabbani;
Majid (Rochester, NY) |
Assignee: |
Eastman Kodak Company
(Rochester, NY)
|
Family
ID: |
27010570 |
Appl.
No.: |
07/588,775 |
Filed: |
September 27, 1990 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
384353 |
Jul 24, 1989 |
|
|
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|
Current U.S.
Class: |
348/391.1;
348/E9.01 |
Current CPC
Class: |
H04N
9/04515 (20180801); G06T 3/4007 (20130101); G06T
3/4015 (20130101); H04N 11/044 (20130101); H04N
9/04557 (20180801) |
Current International
Class: |
G06T
3/40 (20060101); H04N 9/04 (20060101); H04N
11/04 (20060101); H04N 009/64 (); H04N 011/04 ();
H04N 007/12 () |
Field of
Search: |
;358/21R,13,133,12 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Britton; Howard W.
Assistant Examiner: Greening; Wendy R.
Attorney, Agent or Firm: Dugas; Edward
Parent Case Text
This is a division, of application Ser. No. 384,353 filed July 24,
1989.
Claims
We claim:
1. A color image compression apparatus for compressing a color
pixel pattern comprising:
forming means for forming said color pixel pattern into three color
planes of red (R), green (G), and blue (B) pixels;
interpolating means coupled to said forming means for interpolating
missing green pixels from the color plane of green pixels;
processing means for computing the Log R/G and Log B/G using the
interpolated green pixels from said interpolation means;
converting means for converting the uninterpolated green pixels
from said forming means to an intensity uniform space; and
compressing means for compressing the signals from said converting
means and said processing means to provide a compressed color
image.
2. The color image compression system according to claim 1 wherein
said converting means is an L* space converter.
3. The color image compression system according to claim 1 wherein
said processing means is comprised of a lookup table of Log B and
Log G pixel values coupled to a subtractor such that the values of
Log R/G and Log B/G are computed by subtracting the log value of G
from the log value of R and G.
4. The color image compression system according to claim 1 wherein
said compressing means uses a discrete cosine transform based
algorithm.
5. A color image compressing method comprising the steps of:
a. separating a multicolor image into at least three single color
records;
b. interpolating luminance signal values at each spatial location
and corresponding to the red and blue pixel locations;
c. transforming at least one record to a space perceptually uniform
metric;
d. converting two of the remaining records into two color ratios
which are a function of the interpolated luminance signal value at
the spatial locations corresponding to the red and blue pixels;
e. compressing individually the transformed record and said two
color ratios;
f. transmitting said compressed record and said two color
ratios;
g. receiving the compressed record and said two color ratios;
h. interpolating the received compressed record and said two color
ratios to form three color planes of image data; and
i. combining and displaying said three color planes of image data
to form a multicolor image.
6. The color image compression method according to claim 5 wherein
said multicolor image is separated into green, red and blue
records.
7. The color image compression method according to claim 6 wherein
said green record is transformed to a space perceptually uniform
metric.
8. The color image compression method according to claim 7 wherein
said two color ratios are Log R/B.sub.miss and Log
B/G.sub.miss.
9. The color image compression method according to claim 5 wherein
the step of transmitting is deleted and a step of storing said
compressed records substituted therefor.
10. A color image compression method comprising the steps of:
a. converting a multicolor image into red, green and blue planes of
pixel values;
b. forming a set of luminance values for the green plane of pixel
values;
c. interpolating the missing green pixel values at each associated
red and blue pixel value;
d. forming a red color ratio for each red pixel value at a spatial
locating as a function of an associated interpolated missing green
pixel value;
e. forming a blue color ratio for each blue pixel value at a
spatial location as a function of an associated interpolated
missing green pixel value;
f. compressing said formed set of luminance values and said formed
red and blue color ratios; and
g. storing said compressed luminance values and said formed red and
blue color ratios for future use.
11. The color image compression method according to claim 10 and
further comprising the steps of:
decompressing the compressed luminance values and said formed red
and blue color ratios;
interpolating the missing red, green and blue pixel values from
said decompressed luminance values and said formed red and blue
color ratios to form red, green and blue planes of pixel values;
and
displaying the combined red, green and blue planes of pixel values
to reconstruct said multicolor image.
12. A color image compression method comprising the steps of:
a. converting a multicolor image into planes of green, red and blue
pixel values, each associated with a spatial location of said
multicolor image;
b. interpolating the missing green pixel values at each spatial
location corresponding to a red and a blue pixel;
c. forming a color ratio for each red pixel value to its associated
missing green pixel value;
d. forming a color ratio for each blue pixel value to its
associated missing green pixel value;
e. converting the uninterpolated green pixel values to a lightness
space metric;
f. compressing said formed lightness space metric and said formed
color ratios; and
g. storing said compressed formed lightness space metric and said
formed color ratios for future use.
13. The color image compression method according to claim 12
wherein said lightness space metric is an L* space.
14. The color image compression method according to claim 12
wherein step g is deleted and the following steps added:
h. transmitted said compressed formed lightness space metric and
said formed color ratios;
i. receiving said compressed formed lightness space metric and said
formed color ratios;
j. decompressing said compressed formed lightness space metric and
said color ratios;
k. forming planes of red, green and blue pixel values from said
decompressed lightness space metric and said color ratios;
l. combining said planes of red, green and blue pixel values to
form a plane of red, green and blue pixel values; and
m. displaying said formed plane as a reconstructed multicolor
image.
15. The color image compression method according to claim 12
wherein said color ratios are formed as the Log R/G.sub.miss value,
and Log B/G.sub.miss value.
Description
TECHNICAL FIELD
The present invention is directed to a method and an apparatus for
processing image data from a single chip color image sensor to
facilitate visually lossless data compression prior to storage or
transmission.
DESCRIPTION OF THE PRIOR ART
To provide color images, an electronic camera may use a color
beamsplitter and three image sensors, one each for red, green, and
blue. To reduce the size and cost of the camera, a single sensor,
overlaid with a color filter pattern, can be used to obtain color
images, provided that the sensor output signal is processed in an
appropriate manner in order to demultiplex and interpolate the
three color signals.
Many color filter patterns are possible. Some options are described
in the paper entitled "Color Filters and Processing Alternatives
for One-Chip Cameras" by K. A. Parulski published in the IEEE
Transactions on Electronic Devices, Volume ED-32, Number 8, August
1985, Pages 1381-1389.
Single chip color sensors may be used in electronic still cameras
as well as video cameras and camcorders. In order to store high
quality, low noise color images, an electronic camera should
preferrably use digital storage or recording rather than analog
storage. U.S. Pat. No. 4,131,919, "Electronic Still Camera" by
Gareth A. Lloyd and Steven J. Sasson, assigned to Eastman Kodak Co.
the assignee of the present invention, describes an electronic
still camera which uses digital magnetic tape recording. The
digital storage may also be accomplished by using a solid-state
memory card. In all cases, the number of digital images which can
be stored can be increased, and the recording time per picture can
be reduced, if digital data compression is used to reduce the
amount of data required to represent the images.
Conventional data compression methods for images from color cameras
use the demultiplexed and interpolated three-color data instead of
the directly digitized sensor output signal. One example of such an
approach is shown in U.S. Pat. No. 4,797,729 entitled "Systems for
Incorporating an Error-Tolerant Picture Compression Algorithm" by
Y. T. Tsai, which patent is also assigned to Eastman Kodak Co.
The color interpolation process will triple the single "plane" of
data from the digitized sensor output to three planes of data, one
each for the red, green, and blue color information. Each color
plane has the same number of pixels as the sensor. Image
compression can be done directly on these three planes, or more
efficiently, on the YIQ color space which is derived from the RGB
data. After compression, the total amount of data must be much
smaller than the "original" digitized sensor output data, or the
compression will have served no purpose. Inverse compression will
reconstruct the color image in the corresponding color space which
can then be further processed, displayed, or printed.
Since the color interpolation itself does not increase the
information entropy of the original image, but instead increases
the amount of redundant data, conventional compression methods may
be less efficient than directly compressing the digitized sensor
output signal. Considering the implementation cost, system
complexity, and computation time needed, the approach of
compressing the demultiplexed and interpolated data is a poor
choice.
Attention is thus directed to how the compression might be
efficiently accomplished directly on the sensor output data. Since
the CFA introduces a color patterning onto the sensor output data,
the data correlation has many discontinuities of its distribution
which may reduce the compression efficiency. Treating the sensor
output data as a single luminance image data record makes uniform
saturated colors appear as a very busy image which requires a high
bit rate to prevent a visual loss in quality when decompressed.
Furthermore, color image data which has been sparsely sampled
through a color filter array is very sensitive to errors. A single
error in the data may cause a complex error pattern, depending on
the location of the error, the pattern of the color filter array,
and the color interpolation process. Therefore, straight-forward
data compression applied to the color sensor output will not be
efficient, even if the digitized pixel values are divided into
different data records, according to the color filters of the
pixels, and separately compressed, as described for example in U.S.
Pat. No. 4,758,883 "Electronic Picture Camera with Reduced Memory
Capacity" by Kawahara et al. This is because even a slight error in
one of the color channels can cause a significant colored
"artifact" in the nearby region of the image, as a result of the
interpolation process which follows the decompression process.
A new method for compressing one-chip color images, which uses the
sensor output data more judiciously, and introduces the least
complexity in a system implementation, is thus desirable.
SUMMARY OF THE INVENTION
The present invention provides a method and a real-time system
architecture for compressing color images that are obtained from a
single-chip CCD imager of the type which uses a color filter array
(CFA). The first step of the method is to separate the digitized
color sensor output into three data records, one (green) data
records for luminance and two (red and blue) data records for
color. The second step is to convert the red and blue data records
to color ratio data records which are a function of both the red or
blue pixel values and the interpolated luminance (green) signal
value at the spatial locations corresponding to the red or blue
pixels. The third step is to transform the luminance (green) data
record from a linear space metric (that is, a digital quantization
characteristic where equal code value changes correspond to equal
luminance changes in the image, for all code values) to a more
perceptually uniform metric (That is, a digital quantization
characteristic where equal code value changes correspond to equal
changes in the perceived lightness of the image, thereby taking
into account the non-linearity of the human visual system.) for
example, the "L*" metric. The Television and Engineering Handbook
by K. B. Benson Editor, published by McGraw Hill, pages 2.38-2.40
sets forth definitions for uniform color spaces and, more
particularly, the L* space. The fourth step is to individually
compress the luminance (green) data records and the two color ratio
data records (R/G, B/G) to reduce the average bit rate. The
compressed image data may then be recorded or transmitted. In the
playback stage or at the receiving station, an inverse compression
is applied. After decompression, the red, green, and blue data
records are recovered and interpolated to obtain the three full
color planes of digital image data.
This method has the advantage of processing less data than
conventional methods, in less time using less storage, and of
providing more efficient compression.
From the foregoing it can be seen that it is a primary object of
the present invention to perform image compression on color sensor
output data by first rearranging the data into a plurality of data
records in such a way as to reduce the discontinuities in the data
stream.
It is another object of the present invention to process color
image data prior to compression by rearranging and processing the
sensor output data in such a way that compression on the
chrominance information is done on color ratio signals, and
compression on the luminance is done using perceptually uniform
quantization characteristic.
It is yet a further object of the present invention to provide a
method for compressing an image from a one-chip color sensor by
interpolating the luminance record in order to convert the color
data into a form of luminance to color ratios such that errors
generated by compression are not amplified by subsequent
interpolation processes.
These and other objects of the present invention will become more
apparent when taken in conjunction with the following description
and drawings wherein like characters indicate like parts and which
drawings form a part of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic block diagram of an electronic camera
incorporating the present invention.
FIG. 2 is a schematic block diagram of a playback device that may
be used to view images from the electronic camera of FIG. 1.
FIG. 3 illustrates a matrix of color image pixels formed with a
color filter pattern of the type used in the electronic camera of
FIG. 1.
FIG. 4 is a prior art block flow diagram illustrating one
conventional method for compressing a color image.
FIG. 5 is a block flow diagram illustrating a color filter
interpolation method.
FIG. 6A is a block flow diagram illustrating the preferred
embodiment of the camera processing and compression portion of the
present invention.
FIG. 6B is a more detailed block diagram representation of a number
of blocks from FIG. 6A.
FIG. 7 is a block flow diagram illustrating the preferred
embodiment of the playback decompression and processing method
portion of the present invention.
FIG. 8 is a block flow diagram of the preferred compression method
portion of the present invention.
FIG. 9 is a block flow diagram of the preferred decompression
method portion of the present invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION
FIG. 1 illustrates a block diagram of a digital electronic camera.
The image of a scene (not shown) is focused onto an image sensor 20
by a camera lens 10. The image sensor 20 is composed of a number of
discrete sensor elements 20A or pixels arranged in a
two-dimensional array to form individual photosites (see FIG. 3).
The individual photosites are covered with a regular array of color
filters 18. The image sensing elements 20A respond to the
appropriately colored incident illumination to provide an analog
electronic signal corresponding to the intensity of the
illumination incident on the sensor elements. This invention is
described in relation to a charge coupled device (CCD) sensor
having 1280 horizontal columns and 1024 vertical rows of pixels
although other types of image sensors and sensors with other
dimensionalities may be used.
A lens aperture 12 and shutter 14 are interposed between the lens
and image sensor 20 in order to provide the proper sensor
illumination level for a variety of scene light levels. A
birefringent blur filter 16 is placed in front of the image sensor
20 in order to reduce color aliasing. The analog output signal from
the image sensor 20 is processed by an analog signal processor 30
incorporating, for example, a pre-amplifier and a correlated double
sampling circuit which provides a low noise video signal and which
uses circuits which are well known in the art. The output of the
analog signal processor 30 is converted from analog form to digital
form in an A/D converter 32. The A/D converter 32 provides an 8 bit
data signal for each sample.
The digitized color image is processed and compressed in block 40.
The compressed digital image data ID from block 40 is recorded in
the digital recorder block 50 and is available at its output as
stored image data SID. The digital recorder can use any of the
various digital recording techniques which are well known in the
art, for example digital magnetic tape, digital magnetic disk,
optical disk, or solid-state memory cards. A timing and control
circuit 60 controls the various camera components in order to
capture and store an image in response to the pressing of a camera
exposure button (not shown).
Referring now to FIG. 2, there is shown a schematic block diagram
of a device used to display or print images recorded in the
electronic camera of FIG. 1. A digital playback device 70 allows
the stored digital image data SID recorded in the digital recording
block 50 of FIG. 1 to be played back or to play the image data ID
directly from the digital and compression block 40. A digital
decompression and processing device 75 performs the inverse image
compression and the signal processing in order to provide full
resolution RGB data. A video display processing device 80 provides
the specific signal processing needed for a particular video
display 90, for example line rate conversion, video camera
correction and NTSC encoding. A hardcopy print processing device 82
provides the specific signal processing needed for a particular
hardcopy printer 92.
Referring to FIG. 3, there is shown the color filter pattern 18
incorporated in FIG. 1 overlaying the image sensor 20 and the
discrete sensing elements 20A. The 3G (green) CFA (color filter
array) is a 1280.times.1024 pixel pattern of G(green), R(red), and
B(blue) pixels with three quarter G pixels, one eighth R pixels and
one eighth B pixels. The CFA pattern shown in FIG. 3 and the blur
filter 16 shown in FIG. 1 are designed according to the teachings
of U.S. Pat. No. 4,663,661 by J. Weldy et al, entitled "Single
Sensor Color Video Camera With Blurring Filter" which patent is
also assigned to Eastman Kodak Co. A higher proportion of G pixels
relative to R and B is used to improve the luminance resolution of
the image.
The G pixels are arranged in horizontal stripes to reduce rise-time
artifacts, according to the teachings of U.S. patent application
Ser. No. 208,302 entitled, "Color Imaging Apparatus Employing a
Horizontal Stripe Color Filter to Reduce Rise-Time Artifacts" filed
June 17, 1988 by Kenneth A. Parulski, which application is assigned
to Eastman Kodak Co.
Referring to FIG. 4, a prior art block diagram of the digital
processing and compression block 40 of FIG. 1 is shown implemented
with a prior art compression method. Block 9 represents the CFA
image data from the A/D converter 32 of FIG. 1, which consists of
1024.times.1280 or about 1.3 megabytes of data per image. A 3G CFA
interpolation block 11 forms three full 1024.times.1280 planes of
image data for RGB, or approximately 3.9 megabytes of data per
image. Alternately, three planes Y, I and Q may be formed by
directing the RGB image data of block 13 to block 15. Each of the
formed YIQ planes is 1024.times.1280 pixels, the same size as the
RGB image planes of block 13. Image compression is done directly on
the three planes from either block 13 or block 15 in block 17. A
system which incorporates an appropriate form of compression is
shown in U.S. Pat. No. 4,797,729 entitled "Systems Incorporating An
Error Tolerant Picture Compression Algorithm" by Y. T. Tsai.
After compression the total amount of data is substantially
reduced. The compressed data may then be stored in the digital
recording block 50 of FIG. 1 (or alternately may be transmitted).
When played back (or received) an inverse compression operation is
performed as reflected by block 75 of FIG. 2.
The inverse compression reconstructs the color image which can then
be used either for further processing, video display, or
printing.
When using the block flow of FIG. 4, three times the "raw" sensor
data, or 3.9 megabytes, is processed by the image compression block
17. Consequently, the required buffer storage space is increased
and the processing time is lengthened. These two drawbacks are
especially significant when the system is implemented in a
real-time application.
A key question is how the compression can be implemented on the
"raw" sensor data so that the amount of data processed is not
increased. Since the CFA introduces a color pattern into the image
data, the correlation among the adjacent pixel values varies
depending on the color they represent. If the image compression
algorithm treats the "raw" sensor data as luminance data, then it
is a very "busy" image which needs a high bit rate to represent it
with visually lossless quality following color interpolation.
Therefore, unless the "raw" sensor data is sorted out by color the
CFA image compression will not be efficient. Furthermore, a single
error in the uninterpolated image may cause a large error for more
than a single pixel in the final interpolated image. Thus, it has
been concluded that a compression algorithm with good performance
on conventional color interpolated images cannot retain its
performance when it is applied directly to uninterpolated image
data from a single chip color sensor.
In order to fully appreciate the data rearrangements used in the
compression methods to be discussed, a flow chart of the 3G CFA
interpolation block 11 of FIG. 4 is shown in FIG. 5. Color edge
artifacts (color aliasing) at luminance edges are minimized by
interpolating the color signal (red and blue) to luminance (green)
signal ratios instead of linearly interpolating the red and blue
signals.
The missing green pixels are interpolated through the following
equation in block 21: ##EQU1##
The filter coefficients are selected so that the effective PSF
(point spread function) of the interpolated green pixels is equal
to the PSF of the actual green pixels. For simple implementations,
these filter coefficients can be approximated.
A linear-to-log conversion is performed in block 22 and the red
channel horizontal chroma interpolation is performed by block 24
using: ##EQU2##
The red channel vertical chroma interpolation equations are
processed in block 26 as follows: ##EQU3##
Where G.sub.Rmissi 's are the actual green pixel values at the
missing red pixels, and G.sub.1 and G.sub.2 are the interpolated
missing green values at the red and blue photosites.
A log-to-linear process is applied to the signals from block 26 by
the action of block 28.
The blue channel processing uses the same method steps as the red
channel which are represented by the block flow of FIG. 5.
The chroma interpolation (blocks 24 and 26) typically uses a linear
interpolation of the (log R-log G.sub.miss) and (log B-log
G.sub.miss) values, which is equivalent to linearly interpolating
the log R/G.sub.miss and log B/G.sub.miss values.
Refer now to FIG. 6A in conjunction with FIG. 6B which illustrates
the digital processing and compression block 40 of FIG. 1
incorporating the preferred embodiment of the camera processing and
compression portion of the invention. The input to block 41 is the
digitized CFA image data record 9 from the A/D converter 32 in FIG.
1. CFA image data record 9 is a 1280.times.1024.times.8 bit array
of data representing the linearly quantized color sensor data. The
CFA image data record 9 is separated into three color data records
42, 44 and 46. Record 42 is a 1280.times.768.times.8 bit green
array, record 44 is a 640.times.256.times.8 bit red array and
record 46 is a 640.times.256.times.8 bit blue array (where 8 is the
number of bits per pixel).
The green record 42 from block 41 is directed to block 48 wherein
the missing green pixels (G.sub.miss), that is the green values, at
the red and blue photosites, are interpolated in block 481.
Additionally, the log R/G.sub.miss and log B/G.sub.miss values are
computed. This is accomplished using lookup tables 482 and 483 to
perform the logarithmic conversion and a subtraction operation 484,
485 using the well-known equation, log R/G.sub.miss =log R-log
G.sub.miss. The original (uninterpolated) green pixels are then
converted via lookup table 488 to an L* space or another uniform
intensity space, using the relationship L*=116 (Y/Yn) (1/3)-16
where: Y.sub.n is the value of Y for a reference white. The
processing shown in FIG. 6 may be implemented in software in the
digital processing portion of block 40.
Block 52 represents the three processed planes 54, 56 and 58
corresponding to the planes 42, 44 and 46. A compression operation
61 using, for example, an adaptive discrete cosine transform
algorithm is separately performed on the three processed places
from block 52. Once compressed the resulting signals may be stored
(or transmitted) as reflected by block 62.
At playback or upon receiving the transmitted, compressed image,
the compressed signals are processed with the block steps as shown
in FIG. 7. FIG. 7 represents the functions performed by block 75 of
FIG. 2. Inverse compression is performed in block 68. The missing
green, red and blue pixels are interpolated in block 71 which forms
the color image data for the three planes as shown in block 72. The
three planes from block 72 use approximately 3.9 Megabytes of data
to represent an image.
In summary, missing green pixels are first interpolated so that log
B/G.sub.miss and log R/G.sub.miss can be computed and the original
uninterpolated green pixels are converted to L* space such that the
noise sensitivity is distributed uniformly. Since chroma
interpolation uses log R/G and log B/G, the compression on the
chrominance information is preferrably done on this hue ratio so
that the subsequent interpolation after playback does not introduce
color artifacts. The implementation of the conversion to L* space
can be done using a 256.times.8=2K bit lookup table. The log
R/G.sub.miss or log B/G.sub.miss may be implemented using a
256.times.8K bits lookup table and one subtraction process (log
A/B=log A-log B). The compression works on three separate files:
the uninterpolated green pixels and two smaller size log
R/G.sub.miss and log B/G.sub.miss files. In the playback process,
the inverse compression is done first. Then the green pixels are
converted from L* to linear space, interpolated, and converted to
log space such that the missing red and blue pixel interpolation
operation can be performed.
The hardware implementation for the compression function 61 is
illustrated in FIG. 8.
The compressor 61' receives the three digital image input records
"I" from block 52 of FIG. 6 at a block formatter 116 and formats
the image into block I(x,y). The preferred block sizes are either
16.times.16 or 8.times.8 pixels. A two-dimensional discrete cosine
transform is performed by the DCT 118 on each block to generate the
corresponding block T(i,j) of transform (2-D DCT) coefficients.
Since the 2-D DCT is a well known procedure, (see for example U.S.
Pat. No. 4,302,775) no further description will be given herein of
the (2-D DCT) operation. The transform coefficients T(i,j) for each
block are ordered into a one-dimensional array T(k) by an order
coefficients device 120 in order of increasing spatial frequency,
for example by employing a zig-zag scan along diagonals of the
block of coefficients.
Next, the coefficients are adaptively quantized using an adaptive
quantizer 122 in accordance with the visibility of quantization
noise in the presence of image detail within a block. According to
the preferred mode of practicing the invention, the adaptive
quantization 122 is comprised of a normalize coefficients section
124, the output of which is directed to a quantize coefficients
section 126 for accomplishing a variable normalization 124 prior to
a fixed quantization 126. Alternatively, a variable quantization
could be employed. The transform coefficients T(k) are normalized
by dividing each transform coefficient by a normalization factor
N(k) as follows:
where TN(k) is the normalized transform coefficient value. The
normalization factor N(k) is provided by a normalization array
generator 33 receiving as an input display parameters. This is
based on the visibility of quantization noise in the presence of
image detail in the block. The normalized coefficients TN(k) are
quantized in block 126 to form quantized coefficients TN(k). The
quantized coefficients from 126 are encoded in a Huffman and
run-length coding section 128 using a minimum redundancy coding
scheme to produce code values CV(k). The presently preferred coding
scheme is a Huffman code with run-length coding for strings of zero
magnitude coefficients. Since Huffman and run-length coding are
well known in the art, no further description of the coding process
will be given herein. The coded coefficients are directed to and
recorded by the digital recording block 50 of the electronic camera
of FIG. 1.
Referring to FIG. 9, the inverse compressor 68', performs the
inverse of the compression function performed by the compressor 61'
in FIG. 8 to recover the digital image data. The code values CV(k)
are directed to the input of a decode coefficients block 130 to
produce normalized coefficients TN(k). The normalized coefficients
TN(k) are denormalized in block 132 by dividing by the
denormalization values N.sup.-1 (k) that are the inverse of the
normalization array N(k) employed in the transmitter to produce the
denormalized coefficients T(k). Alternatively, the transform
coefficients are denormalized by multiploying by the normalization
coefficients N(k). An inverse normalization array generator 34
provides the signal N.sup.-1 (k).
The one-dimensional string of reconstructed coefficient values T(k)
are re-formated in block 136 into two-dimensional signal block
T(i,j) and the blocks of coefficients are inversely transformed in
block DCT.sup.-1 138 into image values I(x,y). Finally, the block
of image values are re-formated in block 140 into the digital image
I.
While there has been shown what are considered to be the preferred
embodiments of the invention, it will be manifest that many changes
and modifications may be made therein without departing from the
essential spirit of the invention. It is intended, therefore, in
the annexed claims, to cover all such changes and modifications as
may fall within the true scope of the invention.
* * * * *